Flow cytometry is one of the most important tools for high-throughput single cell analysis. Fluorescent labeling acts as the primary approach for cellular analysis in flow cytometry. Nevertheless, fluorescent tags are not applicable to all cases especially small molecules (e.g. metabolites) for which labeling may significantly perturb their properties. Raman spectroscopic signals arising from inherent molecular vibrations provide a key approach to detect specific molecules inside cells and to differentiate cellular state. Raman-based microfluidic devices have been reported. However, the very small cross section of spontaneous Raman scattering results in low Raman signal level and consequently long data acquisition time, which is not compatible with the high- speed flow condition. The long-term goal of the proposed project is to establish a high-throughput high-content single cell analysis platform using molecular fingerprint vibrations as contrast. The specific objective of current application is to develop a vibrational spectroscopic cytometer based on the stimulated Raman scattering (SRS) process. Several recent advances in the Ji-Xin Cheng (PI) lab, including the highly sensitive femtosecond SRS imaging, lock-in free SRS signal detection and a tuned amplifier array for multiplex SRS imaging, pave the foundation for the planned instrumentation. The PI has assembled an interdisciplinary team for the proposed study. Dr. J. Paul Robinson (co-PI) is a leader in development and applications of fluorescence-based flow cytometer and he will bring expertise to the design of fluidics and multichannel detection systems. Dr. Bartek Rajwa (co-PI) will provide expertise for spectroscopic cytometry data analysis and machine learning. The team will design and construct a SRS flow cytometer by multichannel detection of dispersed SRS signal (Aim 1), construct a tandem system able to collect SRS and fluorescence data (Aim 2), develop spectral un-mixing and machine-learning analysis tools able to combine the information obtained from SRS spectra and labeled biomarkers for functional classification of cells (Aim 3), and validate the capability of SRS flow cytometer for label-free detection of single-cell metabolism (Aim 4). With a speed of analyzing thousands of cells per second, SRS flow cytometer will enable high-throughput analysis of single-cell chemical content which is beyond the reach by fluorescence-based flow cytometer.